[None][test] Unwaive DeepSeekV3.2 nvfp4 mtp3_fp8kv_chunked test - #15973
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WalkthroughThe backend now creates a fresh CUDA graph pool handle after graph teardown. Model engine cleanup invokes this rotation after clearing graph runners. One waived DeepSeek integration test entry was removed. ChangesCUDA graph pool lifecycle
Estimated code review effort: 2 (Simple) | ~10 minutes Possibly related PRs
Suggested reviewers: 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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…upstream PR NVIDIA#15973) Squash of upstream PR NVIDIA#15973 (4 commits). One-engine speculative decoders (MTP / DSpark) keep the draft model inside ModelEngine, so PyExecutor.drafter is None; under the overlap scheduler the next draft tokens are produced by the previous batch and their count may not be copied to the C++ request before the next scheduling pass, so the scheduler can budget a gen request as 1 token though the model consumes 1+K -> oversized mixed context/generation batch (total_num_tokens > max_num_tokens). Run scheduler-facing draft-token reservation for every speculative decoder immediately before scheduling (_prepare_scheduler_draft_tokens); placeholder token values are not consumed, their length reserves the correct micro-batch capacity. Includes: skip when spec decode is disabled, handle lightweight executor fixtures, and unwaive the DeepSeekV3 nvfp4 mtp3_fp8kv_chunked test that exposed the bug. Signed-off-by: chungen28 <chung-en@deepinfra.com>
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Unwaive TestDeepSeekV32::test_nvfp4_multi_gpus_piecewise_cuda_graph[mtp3_fp8kv_chunked] (nvbugs/5989920) to re-enable it in CI. Signed-off-by: Yi Zhang <187001205+yizhang-nv@users.noreply.github.com>
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Description
Unwaives
accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus_piecewise_cuda_graph[mtp3_fp8kv_chunked](NVBug 5989920).Two separate defects kept this test failing; both are fixed elsewhere:
total_num_tokens (8195) should be <= max_num_tokens (8192)under one-model MTP with chunked prefill + overlap scheduler. Fixed onmainby [https://nvbugs/6427240][fix] Reserve MTP draft tokens in scheduler for one-model speculative decoding #16101.Backend._graph_pool_handleis class state shared by every engine in the process. Engine teardown resets the graphs captured into that pool, dropping itsuse_countto zero, but the caching allocator only erases the entry once the pool has no blocks left. The next engine in the same worker process then captured into the retired handle and hitit->second->use_count > 0 INTERNAL ASSERT FAILEDinbeginAllocateToPool. Fixed by [https://nvbugs/6475346][fix] Avoid stale CUD… #16952, which removes the shared class handle entirely and lets each capture create its own private pool.This PR depends on #16952 landing first. Verified on 8x B200 that with that class of fix applied, running
[baseline]and[mtp3_fp8kv_chunked]back to back in one pytest session goes from1 failed, 1 passedto2 passed. Without it the test still fails, because the CI stage runs[baseline]first and the MPI workers are reused across both — which is also why the test passes when run standalone.Test Coverage
accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_nvfp4_multi_gpus_piecewise_cuda_graph[mtp3_fp8kv_chunked], re-enabled by this PR, runs inDGX_B200-8_GPUs-PyTorch-*.Post-fix accuracy measured on 8x B200: MMLU 87.865 (reference 87.200), GSM8K 95.072 (reference 95.600).
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